Detection of various types of metal surface defects based on image processing

Abstract

Machine vision is a promising technique to promote intelligent production. It strikes a balance between product quality and production efficiency. However, the existing metal surface defect detection algorithms are too general, and deviate from electrical production equipment in the level of response time to the target image. To address the two problems, this paper designs a detection algorithm for various types of metal surface defects based on image processing. Firstly, each metal surface image was preprocessed through average graying and nonlocal means filtering. Next, the principle of the composite model scale expansion was explained, and an improved EfficientNet was constructed to classify metal surface defects, which couples spatial attention mechanism. Finally, the backbone network of the single shot multi-box detector (SSD) network was improved, and used to fuse the features of the target image. The proposed model was proved effective through experiments.